Summary Mouse

#Quality control metrices

The figures are showing different quality metrices for the control samples.

The table shows the number of cells which were untreated.
Sample_Tag	nCells
Control	17095

The figures are showing different quality metrices for the control samples.

## `geom_smooth()` using formula = 'y ~ x'

The figures are showing different quality metrices for the control samples.

## Warning: Transformation introduced infinite values in continuous x-axis

## Warning: Removed 24 rows containing non-finite values (`stat_density()`).

The figures are showing different quality metrices for the control samples.

The figures are showing different quality metrices for the copanlisib samples.

The table shows the number of cells which were treated with Copanlisib.
Sample_Tag	nCells
Copanlisib	3382

The figures are showing different quality metrices for the copanlisib samples.

## `geom_smooth()` using formula = 'y ~ x'

The figures are showing different quality metrices for the copanlisib samples.

## Warning: Transformation introduced infinite values in continuous x-axis

## Warning: Removed 4 rows containing non-finite values (`stat_density()`).

The figures are showing different quality metrices for the copanlisib samples.

The figures are showing different quality metrices for the alpelisib samples.

The table shows the number of cells which were treated with Alpelisib.
Sample_Tag	nCells
Alpelisib	21531

The figures are showing different quality metrices for the alpelisib samples.

## `geom_smooth()` using formula = 'y ~ x'

The figures are showing different quality metrices for the alpelisib samples.

## Warning: Transformation introduced infinite values in continuous x-axis

## Warning: Removed 12 rows containing non-finite values (`stat_density()`).

The figures are showing different quality metrices for the alpelisib samples.

## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in the
##   data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical variable
##   into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in the
##   data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical variable
##   into a factor?
## The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in the
##   data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical variable
##   into a factor?

#Investigation of unwanted variations - cell cycle phases and mitochondrial gene expression

## Warning: The following features are not present in the object: UBR7, RFC2,
## RAD51, MCM2, TIPIN, MCM6, UNG, POLD3, WDR76, CLSPN, CDC45, CDC6, MSH2, MCM5,
## POLA1, MCM4, RAD51AP1, GMNN, RPA2, CASP8AP2, HELLS, E2F8, GINS2, PCNA, NASP,
## BRIP1, DSCC1, DTL, CDCA7, CENPU, ATAD2, CHAF1B, USP1, SLBP, RRM1, FEN1, RRM2,
## EXO1, CCNE2, TYMS, BLM, PRIM1, UHRF1, not searching for symbol synonyms

## Warning: The following features are not present in the object: NCAPD2, ANLN,
## TACC3, HMMR, GTSE1, NDC80, AURKA, TPX2, BIRC5, G2E3, CBX5, RANGAP1, CTCF,
## CDCA3, TTK, SMC4, ECT2, CENPA, CDC20, NEK2, CENPF, TMPO, HJURP, CKS2, DLGAP5,
## PIMREG, TOP2A, PSRC1, CDCA8, CKAP2, NUSAP1, KIF23, KIF11, KIF20B, CENPE,
## GAS2L3, KIF2C, NUF2, ANP32E, LBR, MKI67, CCNB2, CDC25C, HMGB2, CKAP2L, BUB1,
## CDK1, CKS1B, UBE2C, CKAP5, AURKB, CDCA2, TUBB4B, JPT1, not searching for symbol
## synonyms

## Warning in AddModuleScore(object = object, features = features, name = name, :
## Could not find enough features in the object from the following feature lists:
## S.Score Attempting to match case...Could not find enough features in the object
## from the following feature lists: G2M.Score Attempting to match case...

## Centering and scaling data matrix

## PC_ 1 
## Positive:  Cd69, Gm26870, Olfm4, Gm9733, Retnlg, Ifnb1, Cxcr1, Cd209a, Ly6g, Il23a 
##     Prok2, Cd209f, Vsig4, Hba-ps3, Calm4, Usp17lb, Ifnl2, Siglech, Klri2, Hba-a2 
##     Cd5l, Bcl2l14, Cd209g, Alas2, Ear-ps5, Slc40a1, Cma2, Ngp, Chad, Tcrg-C4 
## Negative:  Rpl36a, Rps16-ps2, Rps24-ps3, Rps10-ps1, Gm19810, Rps19-ps6, Rps3a3, Rps10-ps3, Gm6025, Gm46197 
##     Gm2225, Gm10095, Gm41073, Gm29228, Gm6136, Gm6341, Gm46317, CT025573.1, Gm6109, Gm15896 
##     Rpl26-ps6, Gm5528, Gm44010, Gm7424, Gm10086, Gm14044, Gm8186, Gm19688, Hnrnpa1, Gm7565 
## PC_ 2 
## Positive:  Sparc, Nedd4, Fstl1, Col1a2, Col1a1, Fkbp9, Col3a1, Col5a2, Crtap, Loxl1 
##     Serping1, Bmp1, Serpinh1, Calu, Col5a1, Dcn, Fbn1, Rnase4, Mmp2, Bgn 
##     Pcolce, Adamts2, Fkbp7, Rcn1, Ppic, Serpinf1, Gstm2, Ikbip, Aebp1, Rcn3 
## Negative:  Gm4468, Gm46197, Gm46317, Gm29228, Gm6341, Gm15896, Gm44010, Gm6025, CT025573.1, Gm41073 
##     Gm10095, Rps10-ps3, Gm12739, Rpl26-ps6, Gm2178, Gm5528, Gm9687, Gm14044, Gm13692, AC110534.4 
##     Gm14874, Gm45499, Rpl31-ps9, Gm12788, Rps19-ps6, Rpl12-ps2, Gm5928, Gm11416, AC159898.1, Gm7351 
## PC_ 3 
## Positive:  Pltp, Ctss, Tgfbi, Cybb, Lyz2, Pfn1, Ms4a6c, Ctsc, Ms4a7, Aif1 
##     Msr1, Cd38, C1qb, Dab2, Stab1, F13a1, C1qc, Cd72, Saa3, Ass1 
##     Maf, C1qa, Sdc4, Ehd4, Prdx1, Ly86, Cd74, Fabp5, Apoe, Arg1 
## Negative:  Col14a1, Dpt, Clec3b, Efemp1, Gm45860, Ogn, Ly6c1, Tnxb, Ly6a, Rpl17-ps4 
##     Ackr3, Dpp4, Gm7285, Scara5, Adamts5, Lrrn4cl, Adgrd1, Nid1, Itih5, Rpl31-ps9 
##     C1s1, Lgi2, Dcn, Fndc1, Gm4468, Col3a1, Fstl1, Gm45791, Rpl12-ps2, Gm5928 
## PC_ 4 
## Positive:  Col12a1, Wisp1, Ncam1, Spon1, Lrrc15, C1qtnf3, Cdh11, Mical2, Postn, Thbs2 
##     Hs6st2, Col8a1, Dkk3, Adam12, Ltbp2, Kif26b, Wnt5a, Tagln, Unc5b, Gpr176 
##     Col5a2, Cald1, Adamts12, Tnc, Rflnb, Tpm2, Pmepa1, Olfml3, Col6a3, Bace1 
## Negative:  Cd34, Ly6c1, Clec3b, Tnxb, Efemp1, Ly6a, Dpt, Scara5, Dpp4, Ackr3 
##     Col14a1, Plpp3, Ogn, Cadm3, Cd55, Lrrn4cl, Gas7, Tgfbr2, Adamts5, Igfbp6 
##     Slfn5, Heg1, Sema3c, Efhd1, Adgrd1, Gsn, Itih5, Klf4, Scara3, Uap1 
## PC_ 5 
## Positive:  Esam, Adgrf5, Mmrn2, Flt1, Ptprb, Pdlim1, Tspan7, Plvap, Tie1, Cdh5 
##     Egfl7, Col4a1, Ramp2, Emcn, Col4a2, Gimap4, Aqp1, Spns2, C130074G19Rik, Col15a1 
##     Myct1, Sox18, Palmd, Ece1, Cyyr1, Clec14a, Crip2, Gimap6, Erg, Ablim1 
## Negative:  Ctss, Ms4a6c, Ms4a7, Aif1, Cybb, Pltp, Ctsc, Lyz2, Msr1, C1qc 
##     C1qb, Tgfbi, C1qa, Ly86, Dab2, Cd72, F13a1, Sdc4, Mmp14, Saa3 
##     Cd38, Lgals3bp, Maf, Rgs1, Cd74, Apoe, Gatm, Cx3cr1, Runx3, Stab1

## 18:12:36 UMAP embedding parameters a = 0.9922 b = 1.112

## 18:12:36 Read 31748 rows and found 40 numeric columns

## 18:12:36 Using Annoy for neighbor search, n_neighbors = 30

## 18:12:36 Building Annoy index with metric = cosine, n_trees = 50

## 0%   10   20   30   40   50   60   70   80   90   100%

## [----|----|----|----|----|----|----|----|----|----|

## **************************************************|
## 18:12:44 Writing NN index file to temp file /tmp/RtmpBVuK46/file1c6393a8c845b
## 18:12:44 Searching Annoy index using 1 thread, search_k = 3000
## 18:12:55 Annoy recall = 75.37%
## 18:12:56 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 18:12:59 Initializing from normalized Laplacian + noise (using irlba)
## 18:13:02 Commencing optimization for 200 epochs, with 1606488 positive edges
## 18:13:23 Optimization finished

#Integration

#Clustering analysis

#Celltype identification singleR

#Label comparison

#Transcript analysis

## Rows: 34609 Columns: 4
## ── Column specification ───────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Cell_Index, Labels
## dbl (2): UMIs_human, UMIs_mouse
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

#Celltype identification demultiplexed

## 18:16:11 UMAP embedding parameters a = 0.9922 b = 1.112

## 18:16:11 Read 31748 rows and found 30 numeric columns

## 18:16:11 Using Annoy for neighbor search, n_neighbors = 30

## 18:16:11 Building Annoy index with metric = cosine, n_trees = 50

## 0%   10   20   30   40   50   60   70   80   90   100%

## [----|----|----|----|----|----|----|----|----|----|

## **************************************************|
## 18:16:14 Writing NN index file to temp file /tmp/RtmpBVuK46/file1c63916deed44
## 18:16:14 Searching Annoy index using 1 thread, search_k = 3000
## 18:16:24 Annoy recall = 100%
## 18:16:25 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 18:16:28 Initializing from normalized Laplacian + noise (using irlba)
## 18:16:32 Commencing optimization for 200 epochs, with 1387376 positive edges
## 18:16:50 Optimization finished

##                     Labels
## Xenograft            mainly_human mainly_mouse unique_mouse
##   HN10621                      74         3327         1742
##   HN10960                       1          106           69
##   HN11097_Control               0          576          339
##   HN11097_treated              27          650          329
##   HN15239A_Alpelisib           84         7730         5038
##   HN15239A_Control             29        10127         1500

##                     Labels
## Xenograft            mainly_human mainly_mouse unique_mouse
##   HN10621            2.330855e-03 1.047940e-01 5.486960e-02
##   HN10960            3.149805e-05 3.338793e-03 2.173365e-03
##   HN11097_Control    0.000000e+00 1.814288e-02 1.067784e-02
##   HN11097_treated    8.504473e-04 2.047373e-02 1.036286e-02
##   HN15239A_Alpelisib 2.645836e-03 2.434799e-01 1.586872e-01
##   HN15239A_Control   9.134434e-04 3.189807e-01 4.724707e-02

Summary Mouse

Manuela Benary / Mohammad El-Ismail

2023-03-31